Recall that in (deep) transductive classification we are provided with both a labeled training sample and an unlabeled test sample.
A related but different setting is semi-supervised learning (SSL), where in addition to the labeled training set, we are also given a (typically large) unlabeled training sample. The objective is inductive – namely, to guess the label of any unseen test sample (that was drawn from the same distribution).
A simple observation is that any SSL algorithm can be applied in transduction by using the given (unlabeled) test sample as the unlabeled SSL training sample. Thus, algorithms such as [1]}, [2]}, [3]} can be used to solve transductive learning. This fact may explain a common confusion between SSL and transduction.
Nevertheless, an inductive SSL algorithm does not need to use (and cannot rely on) the fact that it will only be asked about the given test points.
In contrast, a (meaningful) transductive algorithm must rely on this knowledge, as we contend in this paper. In fact, in Section REF  we demonstrate that our proposed transductive procedure substantially outperforms
the SimCLR [1]} and SimCLRv2 [2]} algorithms, where SimCLRv2 currently achieves state-of-the-art SSL performance according to [6]}.
